class_docstr: Samples from a Gumbel Distribution.
  
  Examples::
  
      >>> # xdoctest: +IGNORE_WANT("non-deterministic")
      >>> m = Gumbel(torch.tensor([1.0]), torch.tensor([2.0]))
      >>> m.sample()  # sample from Gumbel distribution with loc=1, scale=2
      tensor([ 1.0124])
  
  Args:
      loc (float or Tensor): Location parameter of the distribution
      scale (float or Tensor): Scale parameter of the distribution
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code: def mean(self):
      return self.loc + self.scale * euler_constant
  
file_path: pytorch/torch/distributions/gumbel.py
imports: import math
  from numbers import Number
  import torch
  from torch.distributions import constraints
  from torch.distributions.transformed_distribution import TransformedDistribution
  from torch.distributions.transforms import AffineTransform, ExpTransform
  from torch.distributions.uniform import Uniform
  from torch.distributions.utils import broadcast_all, euler_constant
label_desc: Use image node_id 4 for calling the Gumbel obj's underlying member method code with example usage: obj.mean() and returns: unknown